



"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE_      						=			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_      						=			'▁'

SCREAMING_SNAKE_CASE_      						=			{'vocab_file': 'sentencepiece.bpe.model'}

SCREAMING_SNAKE_CASE_      						=			{
    'vocab_file': {
        'facebook/nllb-200-distilled-600M': (
            'https://huggingface.co/facebook/nllb-200-distilled-600M/blob/main/sentencepiece.bpe.model'
        ),
    }
}

SCREAMING_SNAKE_CASE_      						=			{
    'facebook/nllb-200-distilled-600M': 10_24,
}

# fmt: off
SCREAMING_SNAKE_CASE_      						=			['ace_Arab', 'ace_Latn', 'acm_Arab', 'acq_Arab', 'aeb_Arab', 'afr_Latn', 'ajp_Arab', 'aka_Latn', 'amh_Ethi', 'apc_Arab', 'arb_Arab', 'ars_Arab', 'ary_Arab', 'arz_Arab', 'asm_Beng', 'ast_Latn', 'awa_Deva', 'ayr_Latn', 'azb_Arab', 'azj_Latn', 'bak_Cyrl', 'bam_Latn', 'ban_Latn', 'bel_Cyrl', 'bem_Latn', 'ben_Beng', 'bho_Deva', 'bjn_Arab', 'bjn_Latn', 'bod_Tibt', 'bos_Latn', 'bug_Latn', 'bul_Cyrl', 'cat_Latn', 'ceb_Latn', 'ces_Latn', 'cjk_Latn', 'ckb_Arab', 'crh_Latn', 'cym_Latn', 'dan_Latn', 'deu_Latn', 'dik_Latn', 'dyu_Latn', 'dzo_Tibt', 'ell_Grek', 'eng_Latn', 'epo_Latn', 'est_Latn', 'eus_Latn', 'ewe_Latn', 'fao_Latn', 'pes_Arab', 'fij_Latn', 'fin_Latn', 'fon_Latn', 'fra_Latn', 'fur_Latn', 'fuv_Latn', 'gla_Latn', 'gle_Latn', 'glg_Latn', 'grn_Latn', 'guj_Gujr', 'hat_Latn', 'hau_Latn', 'heb_Hebr', 'hin_Deva', 'hne_Deva', 'hrv_Latn', 'hun_Latn', 'hye_Armn', 'ibo_Latn', 'ilo_Latn', 'ind_Latn', 'isl_Latn', 'ita_Latn', 'jav_Latn', 'jpn_Jpan', 'kab_Latn', 'kac_Latn', 'kam_Latn', 'kan_Knda', 'kas_Arab', 'kas_Deva', 'kat_Geor', 'knc_Arab', 'knc_Latn', 'kaz_Cyrl', 'kbp_Latn', 'kea_Latn', 'khm_Khmr', 'kik_Latn', 'kin_Latn', 'kir_Cyrl', 'kmb_Latn', 'kon_Latn', 'kor_Hang', 'kmr_Latn', 'lao_Laoo', 'lvs_Latn', 'lij_Latn', 'lim_Latn', 'lin_Latn', 'lit_Latn', 'lmo_Latn', 'ltg_Latn', 'ltz_Latn', 'lua_Latn', 'lug_Latn', 'luo_Latn', 'lus_Latn', 'mag_Deva', 'mai_Deva', 'mal_Mlym', 'mar_Deva', 'min_Latn', 'mkd_Cyrl', 'plt_Latn', 'mlt_Latn', 'mni_Beng', 'khk_Cyrl', 'mos_Latn', 'mri_Latn', 'zsm_Latn', 'mya_Mymr', 'nld_Latn', 'nno_Latn', 'nob_Latn', 'npi_Deva', 'nso_Latn', 'nus_Latn', 'nya_Latn', 'oci_Latn', 'gaz_Latn', 'ory_Orya', 'pag_Latn', 'pan_Guru', 'pap_Latn', 'pol_Latn', 'por_Latn', 'prs_Arab', 'pbt_Arab', 'quy_Latn', 'ron_Latn', 'run_Latn', 'rus_Cyrl', 'sag_Latn', 'san_Deva', 'sat_Beng', 'scn_Latn', 'shn_Mymr', 'sin_Sinh', 'slk_Latn', 'slv_Latn', 'smo_Latn', 'sna_Latn', 'snd_Arab', 'som_Latn', 'sot_Latn', 'spa_Latn', 'als_Latn', 'srd_Latn', 'srp_Cyrl', 'ssw_Latn', 'sun_Latn', 'swe_Latn', 'swh_Latn', 'szl_Latn', 'tam_Taml', 'tat_Cyrl', 'tel_Telu', 'tgk_Cyrl', 'tgl_Latn', 'tha_Thai', 'tir_Ethi', 'taq_Latn', 'taq_Tfng', 'tpi_Latn', 'tsn_Latn', 'tso_Latn', 'tuk_Latn', 'tum_Latn', 'tur_Latn', 'twi_Latn', 'tzm_Tfng', 'uig_Arab', 'ukr_Cyrl', 'umb_Latn', 'urd_Arab', 'uzn_Latn', 'vec_Latn', 'vie_Latn', 'war_Latn', 'wol_Latn', 'xho_Latn', 'ydd_Hebr', 'yor_Latn', 'yue_Hant', 'zho_Hans', 'zho_Hant', 'zul_Latn']




class       snake_case_    (    _lowerCAmelCase					):
							__lowerCAmelCase            =       VOCAB_FILES_NAMES
							__lowerCAmelCase            =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__lowerCAmelCase            =       PRETRAINED_VOCAB_FILES_MAP
							__lowerCAmelCase            =       ["input_ids", "attention_mask"]

							__lowerCAmelCase            =       []
							__lowerCAmelCase            =       []






							def __init__( self      ,  a_      ,  a_="<s>"      ,  a_="</s>"      ,  a_="</s>"      ,  a_="<s>"      ,  a_="<unk>"      ,  a_="<pad>"      ,  a_="<mask>"      ,  a_=None      ,  a_=None      ,  a_=None      ,  a_ = None      ,  a_=None      ,  a_=False      ,  **a_      ,  ):
									# Mask token behave like a normal word, i.e. include the space before it
									a_  :   List[Any]			    =  AddedToken(SCREAMING_SNAKE_CASE_      ,  lstrip=SCREAMING_SNAKE_CASE_      ,  rstrip=SCREAMING_SNAKE_CASE_   ) if isinstance(SCREAMING_SNAKE_CASE_      ,  SCREAMING_SNAKE_CASE_   ) else mask_token

									a_  :   Optional[int]			    =  {} if sp_model_kwargs is None else sp_model_kwargs
									a_  :   Tuple			    =  legacy_behaviour

									super().__init__(
									    bos_token=SCREAMING_SNAKE_CASE_      ,  eos_token=SCREAMING_SNAKE_CASE_      ,  unk_token=SCREAMING_SNAKE_CASE_      ,  sep_token=SCREAMING_SNAKE_CASE_      ,  cls_token=SCREAMING_SNAKE_CASE_      ,  pad_token=SCREAMING_SNAKE_CASE_      ,  mask_token=SCREAMING_SNAKE_CASE_      ,  tokenizer_file=SCREAMING_SNAKE_CASE_      ,  src_lang=SCREAMING_SNAKE_CASE_      ,  tgt_lang=SCREAMING_SNAKE_CASE_      ,  additional_special_tokens=SCREAMING_SNAKE_CASE_      ,  sp_model_kwargs=self.sp_model_kwargs      ,  legacy_behaviour=SCREAMING_SNAKE_CASE_      ,  **SCREAMING_SNAKE_CASE_      ,  )

									a_  :   str			    =  spm.SentencePieceProcessor(**self.sp_model_kwargs   )
									self.sp_model.Load(str(SCREAMING_SNAKE_CASE_   )   )
									a_  :   Optional[Any]			    =  vocab_file
									# Original fairseq vocab and spm vocab must be "aligned":
									# Vocab    |    0    |    1    |   2    |    3    |  4   |  5   |  6   |   7  |   8  |  9
									# -------- | ------- | ------- | ------ | ------- | ---- | ---- | ---- | ---- | ---- | ----
									# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a'
									# spm      | '<unk>' | '<s>'   | '</s>' | 'an'    | '▁n' | '▁m' | '▁t' | '▁k' | '▁a' | '▁s'

									# Mimic fairseq token-to-id alignment for the first 4 token
									a_  :   Tuple			    =  {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

									# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
									a_  :   Optional[Any]			    =  1

									a_  :   Any			    =  len(self.sp_model   )
									a_  :   int			    =  {
									    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(SCREAMING_SNAKE_CASE_   )
									}
									a_  :   Optional[Any]			    =  {v: k for k, v in self.lang_code_to_id.items()}
									a_  :   List[str]			    =  len(self.sp_model   ) + len(self.lang_code_to_id   ) + self.fairseq_offset

									self.fairseq_tokens_to_ids.update(self.lang_code_to_id   )
									a_  :   List[str]			    =  {v: k for k, v in self.fairseq_tokens_to_ids.items()}
									a_  :   Tuple			    =  list(self.lang_code_to_id.keys()   )

									if additional_special_tokens is not None:
											# Only add those special tokens if they are not already there.
											self._additional_special_tokens.extend(
											    [t for t in additional_special_tokens if t not in self._additional_special_tokens]   )

									a_  :   str			    =  src_lang if src_lang is not None else """eng_Latn"""
									a_  :   List[str]			    =  self.lang_code_to_id[self._src_lang]
									a_  :   Any			    =  tgt_lang
									self.set_src_lang_special_tokens(self._src_lang   )






							def __getstate__( self   ):
									a_  :   Union[str, Any]			    =  self.__dict__.copy()
									a_  :   List[Any]			    =  None
									a_  :   Optional[int]			    =  self.sp_model.serialized_model_proto()
									return state






							def __setstate__( self      ,  a_   ):
									a_  :   List[str]			    =  d

									# for backward compatibility
									if not hasattr(self      ,  "sp_model_kwargs"   ):
											a_  :   Dict			    =  {}

									a_  :   Optional[Any]			    =  spm.SentencePieceProcessor(**self.sp_model_kwargs   )
									self.sp_model.LoadFromSerializedProto(self.sp_model_proto   )






							@property
							def snake_case_	( self   ):
									return len(self.sp_model   ) + len(self.lang_code_to_id   ) + self.fairseq_offset + 1  # Plus 1 for the mask token






							@property
							def snake_case_	( self   ):
									return self._src_lang






							@src_lang.setter
							def snake_case_	( self      ,  a_   ):
									a_  :   List[str]			    =  new_src_lang
									self.set_src_lang_special_tokens(self._src_lang   )






							def snake_case_	( self      ,  a_      ,  a_ = None      ,  a_ = False   ):

									if already_has_special_tokens:
											return super().get_special_tokens_mask(
											    token_ids_a=SCREAMING_SNAKE_CASE_      ,  token_ids_a=SCREAMING_SNAKE_CASE_      ,  already_has_special_tokens=SCREAMING_SNAKE_CASE_   )

									a_  :   Optional[int]			    =  [1] * len(self.prefix_tokens   )
									a_  :   Optional[Any]			    =  [1] * len(self.suffix_tokens   )
									if token_ids_a is None:
											return prefix_ones + ([0] * len(SCREAMING_SNAKE_CASE_   )) + suffix_ones
									return prefix_ones + ([0] * len(SCREAMING_SNAKE_CASE_   )) + ([0] * len(SCREAMING_SNAKE_CASE_   )) + suffix_ones






							def snake_case_	( self      ,  a_      ,  a_ = None   ):
									if token_ids_a is None:
											return self.prefix_tokens + token_ids_a + self.suffix_tokens
									# We don't expect to process pairs, but leave the pair logic for API consistency
									return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens






							def snake_case_	( self      ,  a_      ,  a_ = None   ):
									a_  :   str			    =  [self.sep_token_id]
									a_  :   List[Any]			    =  [self.cls_token_id]

									if token_ids_a is None:
											return len(cls + token_ids_a + sep   ) * [0]
									return len(cls + token_ids_a + sep + sep + token_ids_a + sep   ) * [0]






							def snake_case_	( self      ,  a_      ,  a_      ,  a_      ,  a_      ,  **a_   ):
									if src_lang is None or tgt_lang is None:
											raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model"   )
									a_  :   Union[str, Any]			    =  src_lang
									a_  :   Any			    =  self(SCREAMING_SNAKE_CASE_      ,  add_special_tokens=SCREAMING_SNAKE_CASE_      ,  return_tensors=SCREAMING_SNAKE_CASE_      ,  **SCREAMING_SNAKE_CASE_   )
									a_  :   Dict			    =  self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_   )
									a_  :   List[str]			    =  tgt_lang_id
									return inputs






							def snake_case_	( self   ):
									a_  :   Dict			    =  {self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_   ): i for i in range(self.vocab_size   )}
									vocab.update(self.added_tokens_encoder   )
									return vocab






							def snake_case_	( self      ,  a_   ):
									return self.sp_model.encode(SCREAMING_SNAKE_CASE_      ,  out_type=SCREAMING_SNAKE_CASE_   )






							def snake_case_	( self      ,  a_   ):
									if token in self.fairseq_tokens_to_ids:
											return self.fairseq_tokens_to_ids[token]
									a_  :   int			    =  self.sp_model.PieceToId(SCREAMING_SNAKE_CASE_   )

									# Need to return unknown token if the SP model returned 0
									return spm_id + self.fairseq_offset if spm_id else self.unk_token_id






							def snake_case_	( self      ,  a_   ):
									if index in self.fairseq_ids_to_tokens:
											return self.fairseq_ids_to_tokens[index]
									return self.sp_model.IdToPiece(index - self.fairseq_offset   )






							def snake_case_	( self      ,  a_   ):
									a_  :   Optional[Any]			    =  """""".join(SCREAMING_SNAKE_CASE_   ).replace(SCREAMING_SNAKE_CASE_      ,  " "   ).strip()
									return out_string






							def snake_case_	( self      ,  a_      ,  a_ = None   ):
									if not os.path.isdir(SCREAMING_SNAKE_CASE_   ):
											logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""   )
											return
									a_  :   List[str]			    =  os.path.join(
									    SCREAMING_SNAKE_CASE_      ,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]   )

									if os.path.abspath(self.vocab_file   ) != os.path.abspath(SCREAMING_SNAKE_CASE_   ) and os.path.isfile(self.vocab_file   ):
											copyfile(self.vocab_file      ,  SCREAMING_SNAKE_CASE_   )
									elif not os.path.isfile(self.vocab_file   ):
											with open(SCREAMING_SNAKE_CASE_      ,  "wb"   ) as fi:
													a_  :   Union[str, Any]			    =  self.sp_model.serialized_model_proto()
													fi.write(SCREAMING_SNAKE_CASE_   )

									return (out_vocab_file,)






							def snake_case_	( self      ,  a_      ,  a_ = "eng_Latn"      ,  a_ = None      ,  a_ = "fra_Latn"      ,  **a_      ,  ):
									a_  :   int			    =  src_lang
									a_  :   str			    =  tgt_lang
									return super().prepare_seqaseq_batch(SCREAMING_SNAKE_CASE_      ,  SCREAMING_SNAKE_CASE_      ,  **SCREAMING_SNAKE_CASE_   )






							def snake_case_	( self   ):
									return self.set_src_lang_special_tokens(self.src_lang   )






							def snake_case_	( self   ):
									return self.set_tgt_lang_special_tokens(self.tgt_lang   )






							def snake_case_	( self      ,  a_   ):
									a_  :   int			    =  self.lang_code_to_id[src_lang]
									if self.legacy_behaviour:
											a_  :   List[Any]			    =  []
											a_  :   Optional[Any]			    =  [self.eos_token_id, self.cur_lang_code]
									else:
											a_  :   Optional[Any]			    =  [self.cur_lang_code]
											a_  :   Any			    =  [self.eos_token_id]






							def snake_case_	( self      ,  a_   ):
									a_  :   Any			    =  self.lang_code_to_id[lang]
									if self.legacy_behaviour:
											a_  :   List[str]			    =  []
											a_  :   List[str]			    =  [self.eos_token_id, self.cur_lang_code]
									else:
											a_  :   Any			    =  [self.cur_lang_code]
											a_  :   str			    =  [self.eos_token_id]




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
	import PIL


a      :  Dict			     =			logging.get_logger(__name__)







class 				_a				(   _lowerCAmelCase					):
			A      		=	['''pixel_values''']

			def __init__(self,   SCREAMING_SNAKE_CASE_ = True,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = PIL.Image.BICUBIC,   SCREAMING_SNAKE_CASE_ = True,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = 1 / 255,   SCREAMING_SNAKE_CASE_ = True,   SCREAMING_SNAKE_CASE_ = True,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   **SCREAMING_SNAKE_CASE_,   )       ->   None:
						super().__init__(**SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Optional[Any]							      =     size if size is not None else {"""height""": 256, """width""": 256}
						UpperCAmelCase_:		Optional[Any]							      =     get_size_dict(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		int							      =     crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
						UpperCAmelCase_:		Dict							      =     get_size_dict(SCREAMING_SNAKE_CASE_,   param_name="""crop_size"""					)

						UpperCAmelCase_:		int							      =     do_resize
						UpperCAmelCase_:		Any							      =     size
						UpperCAmelCase_:		List[Any]							      =     resample
						UpperCAmelCase_:		int							      =     do_center_crop
						UpperCAmelCase_:		Dict							      =     crop_size
						UpperCAmelCase_:		Tuple							      =     do_rescale
						UpperCAmelCase_:		Tuple							      =     rescale_factor
						UpperCAmelCase_:		Dict							      =     do_normalize
						UpperCAmelCase_:		Dict							      =     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						UpperCAmelCase_:		int							      =     image_std if image_std is not None else IMAGENET_STANDARD_STD

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = PIL.Image.BICUBIC,   SCREAMING_SNAKE_CASE_ = None,   **SCREAMING_SNAKE_CASE_,   )       ->   np.ndarray:
						UpperCAmelCase_:		Tuple							      =     get_size_dict(SCREAMING_SNAKE_CASE_					)
						if "height" not in size or "width" not in size:
									raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'					)
						return resize(
						    SCREAMING_SNAKE_CASE_,   size=(size["""height"""], size["""width"""]),   resample=SCREAMING_SNAKE_CASE_,   data_format=SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = None,   **SCREAMING_SNAKE_CASE_,   )       ->   np.ndarray:
						UpperCAmelCase_:		Optional[int]							      =     get_size_dict(SCREAMING_SNAKE_CASE_					)
						if "height" not in size or "width" not in size:
									raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'					)
						return center_crop(SCREAMING_SNAKE_CASE_,   size=(size["""height"""], size["""width"""]),   data_format=SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = None,   **SCREAMING_SNAKE_CASE_,   )       ->   int:
						return rescale(SCREAMING_SNAKE_CASE_,   scale=SCREAMING_SNAKE_CASE_,   data_format=SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = None,   **SCREAMING_SNAKE_CASE_,   )       ->   np.ndarray:
						return normalize(SCREAMING_SNAKE_CASE_,   mean=SCREAMING_SNAKE_CASE_,   std=SCREAMING_SNAKE_CASE_,   data_format=SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_=None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST,   **SCREAMING_SNAKE_CASE_,   )       ->   PIL.Image.Image:
						UpperCAmelCase_:		Any							      =     do_resize if do_resize is not None else self.do_resize
						UpperCAmelCase_:		Dict							      =     resample if resample is not None else self.resample
						UpperCAmelCase_:		Tuple							      =     do_center_crop if do_center_crop is not None else self.do_center_crop
						UpperCAmelCase_:		str							      =     do_rescale if do_rescale is not None else self.do_rescale
						UpperCAmelCase_:		List[Any]							      =     rescale_factor if rescale_factor is not None else self.rescale_factor
						UpperCAmelCase_:		List[Any]							      =     do_normalize if do_normalize is not None else self.do_normalize
						UpperCAmelCase_:		Optional[Any]							      =     image_mean if image_mean is not None else self.image_mean
						UpperCAmelCase_:		Dict							      =     image_std if image_std is not None else self.image_std

						UpperCAmelCase_:		Any							      =     size if size is not None else self.size
						UpperCAmelCase_:		Optional[Any]							      =     get_size_dict(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Tuple							      =     crop_size if crop_size is not None else self.crop_size
						UpperCAmelCase_:		Optional[int]							      =     get_size_dict(SCREAMING_SNAKE_CASE_,   param_name="""crop_size"""					)

						UpperCAmelCase_:		str							      =     make_list_of_images(SCREAMING_SNAKE_CASE_					)

						if not valid_images(SCREAMING_SNAKE_CASE_					):
									raise ValueError(
									    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
									    """torch.Tensor, tf.Tensor or jax.ndarray."""					)

						if do_resize and size is None or resample is None:
									raise ValueError("""Size and resample must be specified if do_resize is True."""					)

						if do_center_crop and crop_size is None:
									raise ValueError("""Crop size must be specified if do_center_crop is True."""					)

						if do_rescale and rescale_factor is None:
									raise ValueError("""Rescale factor must be specified if do_rescale is True."""					)

						if do_normalize and (image_mean is None or image_std is None):
									raise ValueError("""Image mean and std must be specified if do_normalize is True."""					)

						# All transformations expect numpy arrays.
						UpperCAmelCase_:		Tuple							      =     [to_numpy_array(SCREAMING_SNAKE_CASE_					) for image in images]

						if do_resize:
									UpperCAmelCase_:		int							      =     [self.resize(image=SCREAMING_SNAKE_CASE_,   size=SCREAMING_SNAKE_CASE_,   resample=SCREAMING_SNAKE_CASE_					) for image in images]

						if do_center_crop:
									UpperCAmelCase_:		Dict							      =     [self.center_crop(image=SCREAMING_SNAKE_CASE_,   size=SCREAMING_SNAKE_CASE_					) for image in images]

						if do_rescale:
									UpperCAmelCase_:		Optional[int]							      =     [self.rescale(image=SCREAMING_SNAKE_CASE_,   scale=SCREAMING_SNAKE_CASE_					) for image in images]

						if do_normalize:
									UpperCAmelCase_:		int							      =     [self.normalize(image=SCREAMING_SNAKE_CASE_,   mean=SCREAMING_SNAKE_CASE_,   std=SCREAMING_SNAKE_CASE_					) for image in images]

						UpperCAmelCase_:		Optional[Any]							      =     [to_channel_dimension_format(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					) for image in images]

						UpperCAmelCase_:		Optional[Any]							      =     {"""pixel_values""": images}
						return BatchFeature(data=SCREAMING_SNAKE_CASE_,   tensor_type=SCREAMING_SNAKE_CASE_					)






